scholarly journals Spatio-temporal associations between deforestation and malaria incidence in Lao PDR

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Francois Rerolle ◽  
Emily Dantzer ◽  
Andrew A Lover ◽  
John M Marshall ◽  
Bouasy Hongvanthong ◽  
...  
Keyword(s):  
Forest Cover ◽  
Malaria Incidence ◽  
Lao Pdr ◽  
Short Term ◽  
Modeling Framework ◽  
Temporal Modeling ◽  
Forested Areas ◽  
Spatio Temporal ◽  
The Relationship

As countries in the Greater Mekong Sub-region (GMS) increasingly focus their malaria control and elimination efforts on reducing forest-related transmission, greater understanding of the relationship between deforestation and malaria incidence will be essential for programs to assess and meet their 2030 elimination goals. Leveraging village-level health facility surveillance data and forest cover data in a spatio-temporal modeling framework, we found evidence that deforestation is associated with short-term increases, but long-term decreases confirmed malaria case incidence in Lao People’s Democratic Republic (Lao PDR). We identified strong associations with deforestation measured within 30 km of villages but not with deforestation in the near (10 km) and immediate (1 km) vicinity. Results appear driven by deforestation in densely forested areas and were more pronounced for infections with Plasmodium falciparum (P. falciparum) than for Plasmodium vivax (P. vivax). These findings highlight the influence of forest activities on malaria transmission in the GMS.

scholarly journals Spatio-temporal associations between deforestation and malaria incidence in Lao PDR

2020 ◽  
Author(s):  
Francois Rerolle ◽  
Emily Dantzer ◽  
Andrew A. Lover ◽  
John M. Marshall ◽  
Bouasy Hongvanthong ◽  
...  
Keyword(s):  
Forest Cover ◽  
Malaria Incidence ◽  
Lao Pdr ◽  
Short Term ◽  
Modeling Framework ◽  
Temporal Modeling ◽  
Forested Areas ◽  
Spatio Temporal ◽  
The Relationship

AbstractAs countries in the Greater Mekong Sub-region (GMS) increasingly focus their malaria control and elimination efforts on forest-going populations, greater understanding of the relationship between deforestation and malaria incidence will be essential for programs to assess and meet their 2030 elimination goals. Leveraging village-level health facility surveillance data and forest cover data in a spatio-temporal modeling framework, we found evidence that deforestation is associated with short-term increases, but long-term decreases in confirmed malaria case incidence in Lao People’s Democratic Republic (Lao PDR). We identified strong associations with deforestation measured within 30 km of villages but not with deforestation in the near (10 km) and immediate (1 km) vicinity. Results appear driven by deforestation in densely forested areas and were more pronounced for infections with Plasmodium falciparum (P. falciparum) than for Plasmodium vivax (P. vivax). These findings highlight the influence of forest-going populations on malaria transmission in the GMS.

scholarly journals Assessing the Spatial and Spatio-Temporal Distribution of Forest Species via Bayesian Hierarchical Modeling

Forests ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 573 ◽  
Author(s):  
Óscar Rodríguez de Rivera ◽  
Antonio López-Quílez ◽  
Marta Blangiardo
Keyword(s):  
Hierarchical Modeling ◽  
Temporal Distribution ◽  
Short Term ◽  
Distribution Models ◽  
Address Climate Change ◽  
Bayesian Hierarchical ◽  
Temporal Models ◽  
Spatio Temporal ◽  
The Relationship

Climatic change is expected to affect forest development in the short term, as well as the spatial distribution of species in the long term. Species distribution models are potentially useful tools for guiding species choices in reforestation and forest management prescriptions to address climate change. The aim of this study is to build spatial and spatio-temporal models to predict the distribution of four different species present in the Spanish Forest Inventory. We have compared the different models and showed how accounting for dependencies in space and time affect the relationship between species and environmental variables.

scholarly journals Spatio-temporal associations of air pollutant concentrations, GP respiratory consultations and respiratory inhaler prescriptions: a 5-year study of primary care in the borough of Lambeth, South London

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Mark Ashworth ◽  
◽  
Antonis Analitis ◽  
David Whitney ◽  
Evangelia Samoli ◽  
...  
Keyword(s):  
Primary Care ◽  
Negative Binomial ◽  
Hospital Admissions ◽  
Air Pollutant ◽  
Respiratory Morbidity ◽  
Short Term ◽  
Short Term Exposure ◽  
Short And Long Term ◽  
Spatio Temporal

Abstract Background Although the associations of outdoor air pollution exposure with mortality and hospital admissions are well established, few previous studies have reported on primary care clinical and prescribing data. We assessed the associations of short and long-term pollutant exposures with General Practitioner respiratory consultations and inhaler prescriptions. Methods Daily primary care data, for 2009–2013, were obtained from Lambeth DataNet (LDN), an anonymised dataset containing coded data from all patients (1.2 million) registered at general practices in Lambeth, an inner-city south London borough. Counts of respiratory consultations and inhaler prescriptions by day and Lower Super Output Area (LSOA) of residence were constructed. We developed models for predicting daily PM2.5, PM10, NO2 and O3 per LSOA. We used spatio-temporal mixed effects zero inflated negative binomial models to investigate the simultaneous short- and long-term effects of exposure to pollutants on the number of events. Results The mean concentrations of NO2, PM10, PM2.5 and O3 over the study period were 50.7, 21.2, 15.6, and 49.9 μg/m3 respectively, with all pollutants except NO2 having much larger temporal rather than spatial variability. Following short-term exposure increases to PM10, NO2 and PM2.5 the number of consultations and inhaler prescriptions were found to increase, especially for PM10 exposure in children which was associated with increases in daily respiratory consultations of 3.4% and inhaler prescriptions of 0.8%, per PM10 interquartile range (IQR) increase. Associations further increased after adjustment for weekly average exposures, rising to 6.1 and 1.2%, respectively, for weekly average PM10 exposure. In contrast, a short-term increase in O3 exposure was associated with decreased number of respiratory consultations. No association was found between long-term exposures to PM10, PM2.5 and NO2 and number of respiratory consultations. Long-term exposure to NO2 was associated with an increase (8%) in preventer inhaler prescriptions only. Conclusions We found increases in the daily number of GP respiratory consultations and inhaler prescriptions following short-term increases in exposure to NO2, PM10 and PM2.5. These associations are more pronounced in children and persist for at least a week. The association with long term exposure to NO2 and preventer inhaler prescriptions indicates likely increased chronic respiratory morbidity.

Molecular biology of enzyme adaptations in higher eukaryotes

Genome ◽  
1989 ◽  
Vol 31 (1) ◽  
pp. 272-283 ◽  
Author(s):  
Donal A. Hickey ◽  
Bernhard F. Benkel ◽  
Charalambos Magoulas
Keyword(s):  
Molecular Biology ◽  
Tissue Specificity ◽  
Enzyme System ◽  
Direct Interaction ◽  
Evolutionary Genetics ◽  
Short Term ◽  
Eukaryotic Genes ◽  
Higher Eukaryotes ◽  
The Relationship

Multicellular eukaryotes have evolved complex homeostatic mechanisms that buffer the majority of their cells from direct interaction with the external environment. Thus, in these organisms long-term adaptations are generally achieved by modulating the developmental profile and tissue specificity of gene expression. Nevertheless, a subset of eukaryotic genes are still involved in direct responses to environmental fluctuations. It is the adaptative responses in the expression of these genes that buffers many other genes from direct environmental effects. Both microevolutionary and macroevolutionary patterns of change in the structure and regulation of such genes are illustrated by the sequences encoding α-amylases. The molecular biology and evolution of α-amylases in Drosophila and other higher eukaryotes are presented. The amylase system illustrates the effects of both long-term and short-term natural selection, acting on both the structural and regulatory components of a gene–enzyme system. This system offers an opportunity for linking evolutionary genetics to molecular biology, and it allows us to explore the relationship between short-term microevolutionary changes and long-term adaptations.Key words: gene regulation, molecular evolution, eukaryotes, Drosophila, amylase.

scholarly journals High coverage COVID-19 mRNA vaccination rapidly controls SARS-CoV-2 transmission in long-term care facilities

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Pablo M. De Salazar ◽  
Nicholas B. Link ◽  
Karuna Lamarca ◽  
Mauricio Santillana
Keyword(s):  
Long Term Care ◽  
Mitigation Strategies ◽  
Key Factors ◽  
Modeling Framework ◽  
High Coverage ◽  
Term Care ◽  
Early Effect ◽  
Care Facilities ◽  
The Relationship

Abstract Background Residents of Long-Term Care Facilities (LTCFs) represent a major share of COVID-19 deaths worldwide. Measuring the vaccine effectiveness among the most vulnerable in these settings is essential to monitor and improve mitigation strategies. Methods We evaluate the early effect of the administration of BNT162b2-mRNA vaccine to individuals older than 64 years residing in LTCFs in Catalonia, Spain. We monitor all the SARS-CoV-2 documented infections and deaths among LTCFs residents once more than 70% of them were fully vaccinated (February–March 2021). We develop a modeling framework based on the relationship between community and LTCFs transmission during the pre-vaccination period (July–December 2020). We compute the total reduction in SARS-CoV-2 documented infections and deaths among residents of LTCFs over time, as well as the reduction in the detected transmission for all the LTCFs. We compare the true observations with the counterfactual predictions. Results We estimate that once more than 70% of the LTCFs population are fully vaccinated, 74% (58–81%, 90% CI) of COVID-19 deaths and 75% (36–86%, 90% CI) of all expected documented infections among LTCFs residents are prevented. Further, detectable transmission among LTCFs residents is reduced up to 90% (76–93%, 90% CI) relative to that expected given transmission in the community. Conclusions Our findings provide evidence that high-coverage vaccination is the most effective intervention to prevent SARS-CoV-2 transmission and death among LTCFs residents. Widespread vaccination could be a feasible avenue to control the COVID-19 pandemic conditional on key factors such as vaccine escape, roll out and coverage.

scholarly journals BANK PROFITABILITY AND MACROECONOMY: EVIDENCE FROM LITHUANIA

2017 ◽  
Vol 24 (2) ◽  
pp. 383-405 ◽  
Author(s):  
Laurynas NARUŠEVIČIUS
Keyword(s):  
Banking Sector ◽  
Short Term ◽  
Empirical Examination ◽  
Bank Profitability ◽  
Pooled Mean Group ◽  
Real Gdp ◽  
Income Statement ◽  
Main Determinants ◽  
The Relationship

The purpose of this paper is to investigate the relationship between profitability of the Lithuanian banking sector and its internal and external determinants. We use the panel error correc­tion model to assess long-term and short-term determinants of items from bank income statements (net interest income, net fee and commission income and operating expenses). The results of the pooled mean group estimator show that bank size and real GDP are the main determinants in the long-term. Meanwhile, empirical examination suggests various variables as short-term determinants of income statement items. The pooled mean group estimation technique and the analysis of sepa­rate income statement items enable us to have a better insight into the Lithuanian banking sector and determinants of its revenue and expenses.

scholarly journals Bootstrap ARDL Test on the Relationship among Trade, FDI and CO2 Emissions: Based on the Experience of BRICS Countries

2019 ◽  
Author(s):  
Fumei He ◽  
Ke-Chiun Chang ◽  
Min Li ◽  
Xueping Li ◽  
Fangjhy Li
Keyword(s):  
Foreign Direct Investment ◽  
Causal Relationship ◽  
Co2 Emissions ◽  
Direct Investment ◽  
Export Trade ◽  
Short Term ◽  
Brics Countries ◽  
The Relationship ◽  
Cointegration Relationship

We used the Bootstrap ARDL method to test the relationship between the export trades, FDI and CO2 emissions of the BRICS countries. We found that China's foreign direct investment and the lag one period of CO2 emissions have a cointegration on exports. South Africa's foreign direct investment and CO2 emissions have a cointegration relationship with the lag one period of exports, and South Africa's the lag one period of exports and foreign direct investment have a cointegration relationship with the lag one period of CO2 emissions. But whether it is China or South Africa, these three variables have no causal relationship in the long-term. Among the variables of other BRICS countries, Russia is the only country showed degenerate case #1 in McNown et al. mentioned in their paper. When we examined short-term causality, we found that CO2 emissions and export trade showed a reverse causal relationship, while FDI and carbon emissions were not so obvious. Export trade has a positive causal relationship with FDI. Those variables are different from different situations and different countries.

scholarly journals Connectionist Temporal Modeling of Video and Language: a Joint Model for Translation and Sign Labeling

2019 ◽  
Author(s):  
Dan Guo ◽  
Shengeng Tang ◽  
Meng Wang
Keyword(s):  
Dynamic Programming ◽  
Objective Function ◽  
Visual Representations ◽  
Sequential Learning ◽  
Short Term ◽  
Temporal Modeling ◽  
Temporal Correlations ◽  
Feature Correlation ◽  
3D Cnn

Online sign interpretation suffers from challenges presented by hybrid semantics learning among sequential variations of visual representations, sign linguistics, and textual grammars. This paper proposes a Connectionist Temporal Modeling (CTM) network for sentence translation and sign labeling. To acquire short-term temporal correlations, a Temporal Convolution Pyramid (TCP) module is performed on 2D CNN features to realize (2D+1D)=pseudo 3D' CNN features. CTM aligns the pseudo 3D' with the original 3D CNN clip features and fuses them. Next, we implement a connectionist decoding scheme for long-term sequential learning. Here, we embed dynamic programming into the decoding scheme, which learns temporal mapping among features, sign labels, and the generated sentence directly. The solution using dynamic programming to sign labeling is considered as pseudo labels. Finally, we utilize the pseudo supervision cues in an end-to-end framework. A joint objective function is designed to measure feature correlation, entropy regularization on sign labeling, and probability maximization on sentence decoding. The experimental results using the RWTH-PHOENIX-Weather and USTC-CSL datasets demonstrate the effectiveness of the proposed approach.

scholarly journals Analisis Indeks Harga Konsumen di Indonesia Melalui Pendekatan Kointegrasi

2019 ◽  
Vol 4 (2) ◽  
pp. 110-118
Author(s):  
Muhamad Muin ◽  
Keyword(s):  
Exchange Rate ◽  
Empirical Study ◽  
Money Supply ◽  
Price Index ◽  
Consumer Price Index ◽  
Data Series ◽  
Short Term ◽  
Monthly Data ◽  
The Relationship

This study aims to analyze the relationship between the rupiah exchange rate (RER) and the money supply (M1) on the outgrowth of the consumer price index (CPI) in Indonesia. The data used in this study are monthly data series from January 2005 to January 2019. The results of this empirical study shows that there is a relationship between RER and M1 on CPI in the long term and there is a correction in the short term balance (ECM) which is influenced by M1. All of these variables are significant at α = 5% and partly significant at α = 1%.

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